A decision theoretic framework for approximating concepts
International Journal of Man-Machine Studies
From rough set theory to evidence theory
Advances in the Dempster-Shafer theory of evidence
Information Sciences: an International Journal
Rough sets and intelligent data analysis
Information Sciences—Informatics and Computer Science: An International Journal
Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model
Information Sciences: an International Journal - Special issue: Medical expert systems
Maximal consistent extensions of information systems relative to their theories
Information Sciences: an International Journal
Pearson residuals in multi-way contingency tables
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Information Sciences: an International Journal
Divergence statistics for testing uniform association in cross-classifications
Information Sciences: an International Journal
The superiority of three-way decisions in probabilistic rough set models
Information Sciences: an International Journal
Residual analysis of statistical dependence in multiway contingency tables
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Combinatorics in Pearson residuals
International Journal of Knowledge Engineering and Soft Data Paradigms
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Chance discovery aims at understanding the meaning of functional dependency from the viewpoint of unexpected relations. One of the most important observations is that such a chance is hidden under a huge number of coocurrencies extracted from a given data. On the other hand, conventional data-mining methods are strongly dependent on frequencies and statistics rather than interestingness or unexpectedness. This paper discusses some limitations of ideas of statistical dependence, especially focusing on the formal characteristics of Simpson's paradox from the viewpoint of linear algebra. Theoretical results show that such a Simpson's paradox can be observed when a given contingency table as a matrix is not regular, in other words, the rank of a contingency matrix is not full. Thus, data-ordered evidence gives some limitations, which should be compensated by human-oriented reasoning.